Multi-View Adjacency-Constrained Nearest Neighbor Clustering (Student Abstract)

Authors

  • Jie Yang School of Computer Science, University of Technology Sydney, Australia
  • Chin-Teng Lin School of Computer Science, University of Technology Sydney, Australia Australian AI Institute, University of Technology Sydney, Australia

DOI:

https://doi.org/10.1609/aaai.v36i11.21685

Keywords:

Clustering, Multi-view Learning, Parameter-free

Abstract

Most existing multi-view clustering methods have problems with parameter selection and high computational complexity, and there have been very few works based on hierarchical clustering to learn the complementary information of multiple views. In this paper, we propose a Multi-view Adjacency-constrained Nearest Neighbor Clustering (MANNC) and its parameter-free version (MANNC-PF) to overcome these limitations. Experiments tested on eight real-world datasets validate the superiority of the proposed methods compared with the 13 current state-of-the-art methods.

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Published

2022-06-28

How to Cite

Yang, J., & Lin, C.-T. (2022). Multi-View Adjacency-Constrained Nearest Neighbor Clustering (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13097-13098. https://doi.org/10.1609/aaai.v36i11.21685